I am trying to save some data from MATLAB to excel.
This is very repetitive and I was hoping to use a for loop to significantly reduce the number of lines of code I am using.
To give an example:
APPLES = rand(1,10);
PEARS = rand(1,10);
Horizon = (1:10);
% Writing to excel
xlswrite('Results.xlsx',APPLES,'Apple','B2')
xlswrite('Results.xlsx',Horizon,'Apple','B1')
%Pears
xlswrite('Results.xlsx',PEARS,'Pear','B2')
xlswrite('Results.xlsx',Horizon,'Pear','B1')
The above code will create an excel sheet called Results with two worksheets Apple and Pear with the data I need.
Is it possible to create a for loop to reduce the number of code, as I am actually using 200 fruits, so it starting to take up a lot of space and time to write them by hand?
I have tried to do it by hand which is time-consuming and I have also tried this method which doesn't work:
%test
Apples = rand(1,10);
Pears = rand(1,10);
Horizon = (1:10);
names1 = {'Apples' 'Pears'};
%%
for ii = 1:length(names1)
% Writing to excel
xlswrite('Results22.xlsx',fprintf(names1{ii}),names1{ii},'B2')
xlswrite('Results22.xlsx',Horizon,names1{ii},'B1')
end
The problem is that inside the loop you call the string that represents the name of the variable, instead of the variable itself.
For example, fprintf(names1{ii}) gives the string 'Apples', instead of the variable Apples, that contain the values.
To resolve that, and for good practice of not holding 200 fruits in workspace, you can define the fruits as fields of struct. Then, you can access it with dynamic field name:
fruit_names = {'Apples', 'Pears'};
Horizon = (1:10);
for k=1:length(fruit_names)
fruit.(fruit_names{k}) = rand(1,10);
xlswrite('Results22.xlsx',fruit.(fruit_names{k}),fruit_names{k},'B2')
xlswrite('Results22.xlsx',Horizon,fruit_names{k},'B1')
end
Related
I have raw data in a .mat format. The Data is comprised of a bunch of Structures titled 'TimeGroup_XX' where xx is just a random number. Each one of these structures contains a bunch of signals and their corresponding time steps.
It looks something like this
TimeGroup_45 =
time45: [34069x1 double]
Current_Shunt_5: [34069x1 double]
Voltage_Load_5: [34069x1 double]
This is simply unusable, as I simply do not know where the variable I am looking for is hiding in 100's of the structures contained in the raw data. I just know that I am looking for 'Current_Shut_3' for example!
There has to be a way that would allow me to do the following
for all variables in Work space
I_S3 = Find(Current_Shut_3)
end for
Basically I do not want to manually click through every structure to find my variable and just want it to be saved in a normal time series instead of it being hidden in a random structure! Any suggestion on how to do it? There has to be a way!
I have tried using the 'whos' command, but did not get far, as it only returns a list of the stored strucutres in the workspace. I cannot convert that text to a variable and tell it to search for all the fields.
Thanks guys/girls!
This is a great example of why you shouldn't iterate variable names when there are plenty of adequate storage methods that don't require code gymnastics to get data back out of. If you can change this, do that and don't even bother reading the rest of this answer.
Since everything is apparently contained in one *.mat file, specify an output to load so it's output into a unified structure and use fieldnames to iterate.
Using the following data set, for example:
a1.Current_Shunt_1 = 1;
a2.Current_Shunt_2 = 2;
a5.Current_Shunt_5 = 5;
b1.Current_Shunt_5 = 10;
save('test.mat')
We can do:
% Load data
alldata = load('test.mat');
% Get all structure names
datastructs = fieldnames(alldata);
% Filter out all but aXX structures and iterate through
adata = regexpi(datastructs, 'a\d+', 'Match');
adata = [adata{:}];
queryfield = 'Current_Shunt_5';
querydata = [];
for ii = 1:numel(adata)
tmp = fieldnames(alldata.(adata{ii}));
% See if our query field is present
% If yes, output & break out of loop
test = intersect(queryfield, tmp);
if ~isempty(test)
querydata = alldata.(adata{ii}).(queryfield);
break
end
end
which gives us:
>> querydata
querydata =
5
I have a huge table data= {1000 x 1000} of binary data.
They table's variable names are encoded for eg D1,D2,...,DA2,DA3,... with their real labels given in a .txt file.
The .txt file also consists of some text for eg:
D1: Age
Mean age: 33
Median :
.
.
.
D2: weight
I would just like to pick out these names from the text file and create a table with the real variable names.
Any suggestions?
If there is a specific number of lines between each of those labels, then you can extract them by reading in the file, and looping over the relevant lines. For each label, it simple to extract the label with strsplit()
e.g. Let's say there's 5 lines between each label
uselessLines = 5;
% imports as a vertical matrix with each line from the file.
dataLabelsFile = importdata(filename);
% get the total number of lines
numLines = size(dataLabelsFile);
% pre-allocate array for labels, a cell is used for a string
dataLabels = cell(ceil(numLines/(uselessLines+1)));
% use a seperate counting variable
m = 1;
% now, for each label, we add it to the dataLabels matrix
for i=1:(uselessLines+1):numLines
line = strsplit(dataLabelsFile{i}); % by default splits on whitespace
dataLabels(m) = line(2);
m = m + 1;
end
By the end of that loop you should have a variable called dataLabels that holds all of the labels. Now, you can actually very easily work out which label goes with which set of data
provided they are still in the same order. The indexes will be the same for the label to the data.
This is a method you could try if the labels are evenly spaced.
However, if the labels are a random number of lines, then you probably want to do a check with a regular expression like the person below me has suggested. Then you just replace the last two lines of the loop with something like this.
...
if (regular expression matched)
dataLabels(m) = line(2);
m = m + 1;
end
...
That being said, while regular expressions are flexible, if you can get away with replacing it with literally one function call, it's usually better to do that. Regex efficiencies are determined by the skill of the programmer, while in-built functions have generally been tested by some of the better programmers in the world. Additionally, Regex's are harder to understand if you ever want to go back and change it.
Of course there are times when Regex's are amazing, I'm just not convinced this is one of those times.
An implemention of the approach in my earlier comment:
fid = fopen(filename);
varNames = cell(0);
proceed = true;
while proceed
line = fgetl(fid);
if ischar(line)
startIdx = regexp(line,'(?<=^[A-Z]*\d*:)\s');
if ~isempty(startIdx)
varNames{end+1} = strtrim(line(startIdx:end)); %#ok<SAGROW>
end
else
proceed = false;
end
end
fclose(fid);
I cant put the resulting varNames in a table for you, since I have a version of Matlab that does not support tables.
I am creating a dummy example for my situation. Real problem is way more complicated.
Dummy Example:
I have a struct of size 2 with an entry called 'name' and an array of size 2x1.
So, the Struct called Store looks like:
Store(1).name = 'Apple';
Store(1).Data = [1; 2];
Store(2).name = 'Orange';
Store(2).Data = [24; 57];
I want to print this to excel such that it looks like the following:
Apple 1
Apple 2
Orange 24
Orange 57
I don't even know how to start with the above example. I have used xlswrite in the past but never for mixed type data.
If all your Data fields are of size 2x1, you should be able to the following:
% prepare cell to hold your data
xlData = cell(2*numel(store), 2);
% fill column 1:
xlData(1:2:end-1,1) = {store.name};
xlData(2:2:end,1) = xlData(1:2:end-1);
% fill column 2:
xlData(:,2) = num2cell(vertcat(store.Data));
% write to excel:
xlswrite('yourExcelfile.xlsx', xlData)
Got no Matlab at hand to test, but it should help you get going.
Since xlswrite takes a cell-array, there's no issue with mixed datatypes.
I wish to apply the same data analysis to multiple data time series. However the number of data series is variable. So instead of hard-coding each series' analysis I would like to be able to specify the number and name of the funds and then have the same data-manipulation done to all before they are combined into a single portfolio.
Specifically I have an exel file where each worksheet is a time series where the first column is dates and the second column is prices. The dates for all funds may not correspond so the individual worksheets must be sifted for dates that occur in all the funds before combining into one data set where there is one column of dates and all other columns correspond to the data of each of the present funds.
This combined data set is then analysed for means and variances etc.
I currently have worked out how to carry out the merging and the analysis (below) but I would like to know how I can simply add or remove funds (i.e. by including new worksheets containing individual funds data in the excel file) without having to re-write and add/ remove extra/excess matlab code.
*% LOAD DATA*
XL='XLData.xlsx';
formatIn = 'dd/mm/yyyy';
formatOut = 'mmm-dd-yyyy';
*%SPECIFY WORKSHEETS*
Fund1Prices=3;
Fund2Prices=4;
*%RETRIEVE VALUES*
[Fund1values, ~, Fund1sheet] = xlsread(XL,Fund1Prices);
[Fund2values, ~, Fund2sheet] = xlsread(XL,Fund2Prices);
*%EXTRACT DATES AND DATA AND COMBINE (TO REMOVE UNNECCESSARY TEXT IN ROWS 1
%TO 4) FOR FUND 1.*
Fund1_dates_data=Fund1sheet(4:end,1:2);
Fund1Dates= cellstr(datestr(datevec(Fund1_dates_data(:,1),formatIn),formatOut));
Fund1Data= cell2mat(Fund1_dates_data(:,2));
*%EXTRACT DATES AND DATA AND COMBINE (TO REMOVE UNNECCESSARY TEXT IN ROWS 1
%TO 4) FOR FUND 2.*
Fund2_dates_data=Fund2sheet(4:end,1:2);
Fund2Dates= cellstr(datestr(datevec(Fund2_dates_data(:,1),formatIn),formatOut));
Fund2Data= cell2mat(Fund2_dates_data(:,2));
*%CREATE TIME SERIES FOR EACH FUND*
Fund1ts=fints(Fund1Dates,Fund1Data,'Fund1');
Fund2ts=fints(Fund2Dates,Fund2Data,'Fund2');
*%CREATE PORTFOLIO*
Port=merge(Fund1ts,Fund2ts,'DateSetMethod','Intersection');
*%ANALYSE PORTFOLIO*
Returns=tick2ret(Port);
q = Portfolio;
q = q.estimateAssetMoments(Port)
[qassetmean, qassetcovar] = q.getAssetMoments
Based on edit to the question, the answer was rewritten
You can put your code into a function. This function can be saved as an .m-file and called from Matlab.
However, you want to replace the calls to specific worksheets (Fund1Prices=3) with an automated way of figuring out how many worksheets there are. Here's one way of how to do that in a function:
function [Returns,q,qassetmean,qassetcovar] = my_data_series_analysis(XL)
% All input this function requires is a variable
% containing the name of the xls-file you want to process
formatIn = 'dd/mm/yyyy';
formatOut = 'mmm-dd-yyyy';
% Determine the number of worksheets in the xls-file:
[~,my_sheets] = xlsfinfo(XL);
% Loop through the number of sheets
% (change the start value if the first sheets do not contain data):
% this is needed to merge your portfolio
% in case you do not start the for-loop at I=1
merge_count = 1;
for I=1:size(my_sheets,2)
% RETRIEVE VALUES
% note that Fund1Prices has been replaced with the loop-iterable, I
[FundValues, ~, FundSheet] = xlsread(XL,I);
% EXTRACT DATES AND DATA AND COMBINE
% (TO REMOVE UNNECCESSARY TEXT IN ROWS 1 TO 4)
Fund_dates_data = FundSheet(4:end,1:2);
FundDates = cellstr(datestr(datevec(Fund_dates_data(:,1),...
formatIn),formatOut));
FundData = cell2mat(Fund_dates_data(:,2));
% CREATE TIME SERIES FOR EACH FUND
Fundts{I}=fints(FundDates,FundData,['Fund',num2str(I)]);
if merge_count == 2
Port = merge(Fundts{I-1},Fundts{I},'DateSetMethod','Intersection');
end
if merge_count > 2
Port = merge(Port,Fundts{I},'DateSetMethod','Intersection');
end
merge_count = merge_count + 1;
end
% ANALYSE PORTFOLIO
Returns=tick2ret(Port);
q = Portfolio;
q = q.estimateAssetMoments(Port)
[qassetmean, qassetcovar] = q.getAssetMoments
This function will return the Returns, q, qassetmean and qassetcovar variables for all the worksheets in the xls-file you want to process. The variable XL should be specified like this:
XL = 'my_file.xls';
You can also loop over more than one xls-file. Like this:
% use a cell so that the file names can be of different length:
XL = {'my_file.xls'; 'my_file2.xls'}
for F=1:size(XL,1)
[Returns{F},q{F},qassetmean{F},qassetcovar{F}] = my_data_series_analysis(XL{F,1});
end
Make sure to store the values which are returned from the function in cells (as shown) or structs (not shown) to account for the fact that there may be a different number of sheets per file.
I need to calculate the mean, standard deviation, and other values for a number of variables and I was wondering how to use a loop to my advantage. I have 5 electrodes of data. So to calculate the mean of each I do this:
mean_ch1 = mean(ch1);
mean_ch2 = mean(ch2);
mean_ch3 = mean(ch3);
mean_ch4 = mean(ch4);
mean_ch5 = mean(ch5);
What I want is to be able to condense that code into a line or so. The code I tried does not work:
for i = 1:5
mean_ch(i) = mean(ch(i));
end
I know this code is wrong but it conveys the idea of what I'm trying to accomplish. I want to end up with 5 separate variables that are named by the loop or a cell array with all 5 variables within it allowing for easy recall. I know there must be a way to write this code I'm just not sure how to accomplish it.
You have a few options for how you can do this:
You can put all your channel data into one large matrix first, then compute the mean of the rows or columns using the function MEAN. For example, if each chX variable is an N-by-1 array, you can do the following:
chArray = [ch1 ch2 ch3 ch4 ch5]; %# Make an N-by-5 matrix
meanArray = mean(chArray); %# Take the mean of each column
You can put all your channel data into a cell array first, then compute the mean of each cell using the function CELLFUN:
meanArray = cellfun(#mean,{ch1,ch2,ch3,ch4,ch5});
This would work even if each chX array is a different length from one another.
You can use EVAL to generate the separate variables for each channel mean:
for iChannel = 1:5
varName = ['ch' int2str(iChannel)]; %# Create the name string
eval(['mean_' varName ' = mean(' varName ');']);
end
If it's always exactly 5 channels, you can do
ch = {ch1, ch2, ch3, ch4, ch5}
for j = 1:5
mean_ch(j) = mean(ch{j});
end
A more complicated way would be
for j = 1:nchannels
mean_ch(j) = eval(['mean(ch' num2str(j) ')']);
end
Apart from gnovice's answer. You could use structures and dynamic field names to accomplish your task. First I assume that your channel data variables are all in the format ch* and are the only variables in your MATLAB workspace. The you could do something like the following
%# Move the channel data into a structure with fields ch1, ch2, ....
%# This could be done by saving and reloading the workspace
save('channelData.mat','ch*');
chanData = load('channelData.mat');
%# Next you can then loop through the structure calculating the mean for each channel
flds = fieldnames(chanData); %# get the fieldnames stored in the structure
for i=1:length(flds)
mean_ch(i) = mean(chanData.(flds{i});
end